A common configuration of levelling machine has a set of work rolls, with axes parallel and displaced horizontally, which is mounted in a fixed frame and supported by larger diameter backup rolls in such a way as to minimise bending deflections. These deflections are caused by the large forces applied to the workpiece in the levelling operation. A second set of work rolls, mounted in an adjustable frame, has its rolls parallel to those in the first frame and offset so that the two sets of rolls may be intermeshed to create a workpiece path which causes repeated reverse bending as the workpiece is driven through the machine. A number of alternative arrangements are feasible for moving the workpiece through the leveller from pulling it through from the exit end, to driving one or more work rolls by speed-controlled electrical or hydraulic motors.
The prime function of the hot levelling equipment is to improve the flatness and surface finish of the rolled material (referred to as a pattern). During rolling, non-uniform thickness reductions in the transverse direction (typically in the range 0-0.2%) result in corresponding elongation variations which are manifested in the form of undesirable waves or buckles. End effects also produce flatness imperfections in the form of buckles or plate end curvature.
The improvement in flatness in the levelling equipment is achieved by passing the "buckled" or "wavy" pattern between a series of driven, intermeshing rolls which produce bending stresses in excess of the material yield strength. Small longitudinal strains occur preferentially in non-buckled regions and the net effect is to eliminate a significant proportion of the buckling present after rolling.
It has been found that to achieve the best results, a mathematical model of the process is necessary to predict the optimum machine settings for the hot leveller. This allows optimum flatness and avoids overloading of the machine elements or drive system. Because the theory of hot levelling is not well developed, a number of empirical correction factors, whose values are determined on the basis of experience, are preferably used in the model. Some automatic compensation for this potential source of error can be achieved by adapting the models during processing on the basis of comparisons made between predicted and measured quantities. This technique offers an extremely powerful tool for improving the prediction of optimum settings.